How to Architect a Personalized Ads Serving System

Q: How to Architect a Personalized Ads Serving System

This question evaluates a data scientist's competency in designing end-to-end personalized advertising systems within the Machine Learning domain, covering system-level ML architecture, feature engineering and stores, multi-stage model pipelines, real-time serving, experimentation, and evaluation under latency and scale constraints.

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Question

Full-Funnel Ads Serving System Design

Scenario

You are asked to architect a full-funnel advertising platform that serves personalized ads to users on a social media app. The system should maximize long-term value by balancing user experience and advertiser outcomes under latency and scale constraints.

Task

Design an end-to-end ads serving system. Address:

Data collection and event schema
Feature engineering and feature store (offline/online parity)
Model architecture: retrieval → ranking → re-ranking
Real-time serving and latency budgets
Feedback loops and training pipelines
Exploration vs. exploitation strategies
A/B testing design and evaluation
Offline and online metrics to track
Cold-start handling for users and ads

Assume standard ad objectives (e.g., CPC/CPA) and typical mobile feed constraints.

Hints

Think multi-stage candidate generation (retrieval → ranking → re-ranking)
Latency budgets and fallbacks per stage
Point-in-time correct joins in the feature store
Bandits for exploration/exploitation
Calibration and counterfactual evaluation for offline metrics

How to Architect a Personalized Ads Serving System

Full-Funnel Ads Serving System Design

Scenario

Task

Hints

Solution

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